Most people are familiar with the current user experience of walking through security at an airport prior to departure. After flight check-in, machines deep inside the airport scan checked bags to detect explosives. Since prohibited items in checked baggage almost exclusively encompass explosives, the machines can scan these bags more efficiently than at the checkpoint.
Next, the passenger goes to a Transportation Security Administration (TSA) checkpoint, where a machine, such as an x-ray or CT scanner, scans carry-on objects, like a bag, laptop, equipment, or a tray containing various items. Today's standard scanners across most airports use single-view x-ray technology, through which operators see a top-down view of baggage as it passes through the machine. Recent innovations in imaging hardware include multi-view x-rays, multi-spectral x-rays, and CT technology to provide 3-dimensional, or other multi-dimensional views of baggage. Using any of these technologies, human screening operators seek to find prohibited items including firearms, knives and other sharps, explosives, liquids, and other prohibited items.
More specifically, as a bag enters the scanner, the device captures an image of the bag, representing positional elements (coordinates), z-effective number, and x-ray attenuation, among other data. Using this data, material and density approximations are determined. The image is projected onto a screen, often color-coded to indicate the type of material that each item could possibly be comprised of, based on the density approximations (organic, metal, etc.). This image is analyzed by a human screening officer, whose job it is to identify any prohibited items in the bag. The human operator has a few inputs including the opportunity to start or stop the conveyor belt, and the ability to change the coloring of the image to highlight metallic or organic items (e.g., based on density).
Human-assist tools that attempt to automatically identify threats in the bag are not widely deployed, and most checkpoint scanners do not make use of them. The few Automated Threat Recognition algorithms that do exist use outdated algorithms for comparing regions to a threat database (“pixel matching” or similar), or hard-coded basic metrics for determining threatening areas (e.g. very high-density regions that could be explosives). These systems are often closed, with the only inputs being the operator, the x-ray, and maybe a basic classification engine and they are not connected to any broader network. These systems also do not communicate—the AIT scanner, explosive trace detector, and baggage scanner exist separately, unaware of each other's assessment of the same passenger or their baggage.
In 2016, the US Transportation Security Agency (TSA) screened over 700 million passengers, 450 million checked bags, and 1.6 billion carry-on bags. In those bags, the TSA discovered around 3,000 firearms, 80% of which were loaded. In a world where a plane can be overtaken with small knives, every threat is a potential disaster. Firearms detected by the TSA are expected to increase 15-20% per year. With passengers expected to increase 4-10% per year, the problem will only worsen.
Published research shows that humans are simply not effective at this task called “sporadic visual search.” Human performance increases the longer a human spends on the tasks, and also decreases as the frequency of threats decrease—in other words, the less often they see something, the less they are expecting it. Department of Homeland Security audits in 2015 found that 95% of threats got through TSA screening officers. Human scanning operators work long shifts and currently are swapped out every 20 minutes on the scanner to avoid performance dropping off to the point where almost anything but the most obvious of threats could get through. These swap-outs are time consuming and expensive for the TSA.
Because humans are inherently poor at sporadic visual search, one of the biggest slowdowns in the security lines is caused by an operator manually pausing the belt, re-scanning bags, and otherwise taking their time while scanning for threats. On the national stage, it has been reported that this lack of efficiency leads to $4B in economic losses and 40,000 lost jobs due to slowdowns, in addition to a loss of consumer confidence in our national security. To make matters worse, the volume of passengers nationwide is projected to double over the next two decades.
Current software solutions are focused on explosives detection in checked baggage using techniques nearly a decade old. Both the original equipment manufacturers and government forces are eager for advanced detection to aid in the discovery of sharps, firearms, explosives, and other prohibited items in carry-on baggage, in addition to safely classifying innocuous items to quickly clear bags through.
One of ordinary skill in the art will appreciate these as well as numerous other aspects in reading the following disclosure.
The drawings are for illustrating example embodiments, and the inventions are not limited to the arrangements and instrumentality shown in the drawings.